@inproceedings{potta-etal-2024-attrisage,
title = "{A}ttri{S}age: Product Attribute Value Extraction Using Graph Neural Networks",
author = "Potta, Rohan and
Asthana, Mallika and
Yadav, Siddhant and
Goyal, Nidhi and
Patnaik, Sai and
Jain, Parul",
editor = "Falk, Neele and
Papi, Sara and
Zhang, Mike",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-srw.8",
pages = "89--94",
abstract = "Extracting the attribute value of a product from the given product description is essential for ecommerce functions like product recommendations, search, and information retrieval. Therefore, understanding products in E-commerce. Greater accuracy certainly gives any retailer the edge. The burdensome aspect of this problem lies in the diversity of the products and their attributes and values. Existing solutions typically employ large language models or sequence-tagging approaches to capture the context of a given product description and extract attribute values. However, they do so with limited accuracy, which serves as the underlying motivation to explore a more comprehensive solution. Through this paper, we present a novel approach for attribute value extraction from product description leveraging graphs and graph neural networks. Our proposed method demonstrates improvements in attribute value extraction accuracy compared to the baseline sequence tagging approaches.",
}
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<abstract>Extracting the attribute value of a product from the given product description is essential for ecommerce functions like product recommendations, search, and information retrieval. Therefore, understanding products in E-commerce. Greater accuracy certainly gives any retailer the edge. The burdensome aspect of this problem lies in the diversity of the products and their attributes and values. Existing solutions typically employ large language models or sequence-tagging approaches to capture the context of a given product description and extract attribute values. However, they do so with limited accuracy, which serves as the underlying motivation to explore a more comprehensive solution. Through this paper, we present a novel approach for attribute value extraction from product description leveraging graphs and graph neural networks. Our proposed method demonstrates improvements in attribute value extraction accuracy compared to the baseline sequence tagging approaches.</abstract>
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%0 Conference Proceedings
%T AttriSage: Product Attribute Value Extraction Using Graph Neural Networks
%A Potta, Rohan
%A Asthana, Mallika
%A Yadav, Siddhant
%A Goyal, Nidhi
%A Patnaik, Sai
%A Jain, Parul
%Y Falk, Neele
%Y Papi, Sara
%Y Zhang, Mike
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F potta-etal-2024-attrisage
%X Extracting the attribute value of a product from the given product description is essential for ecommerce functions like product recommendations, search, and information retrieval. Therefore, understanding products in E-commerce. Greater accuracy certainly gives any retailer the edge. The burdensome aspect of this problem lies in the diversity of the products and their attributes and values. Existing solutions typically employ large language models or sequence-tagging approaches to capture the context of a given product description and extract attribute values. However, they do so with limited accuracy, which serves as the underlying motivation to explore a more comprehensive solution. Through this paper, we present a novel approach for attribute value extraction from product description leveraging graphs and graph neural networks. Our proposed method demonstrates improvements in attribute value extraction accuracy compared to the baseline sequence tagging approaches.
%U https://aclanthology.org/2024.eacl-srw.8
%P 89-94
Markdown (Informal)
[AttriSage: Product Attribute Value Extraction Using Graph Neural Networks](https://aclanthology.org/2024.eacl-srw.8) (Potta et al., EACL 2024)
ACL
- Rohan Potta, Mallika Asthana, Siddhant Yadav, Nidhi Goyal, Sai Patnaik, and Parul Jain. 2024. AttriSage: Product Attribute Value Extraction Using Graph Neural Networks. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 89–94, St. Julian’s, Malta. Association for Computational Linguistics.